Create insights on combined data sets, without revealing your sensitive input data. This way, data like personal or competitive information, can be used in a trusted and privacy-preserving manner. Data which is too sensitive to be shared becomes accessible for joint analyses.
Decentralized analytics on encrypted data is faster to implement compared to traditional ways of centralizing data for computations. Both technical and legal complexity are reduced, speeding up the implementation of data collaborations.
We use Secure Multi-Party Computation (MPC), a state of the art privacy technology. Since sensitive input data are not exposed by design, compliance with abstract notions in the GDPR becomes elegantly simple compared to traditional data sharing.
Unlocking industry collaboration with uncompromised privacy
In this first video of the series, our CTO Niek explains the basics of our data analytics solution. Our technology is based on MPC, a very powerful technology that allows you to run your logic across multiple data sources. Watertight, always encrypted.
We introduce secure multi-party computation (MPC), a paradigm that enables multiple parties to perform computations on their joint data set, such that each party learns nothing beyond the output of these computations.
The European Commission and the United Nations sponsored three studies that explain that a technique called Secure Multi-Party Computation (MPC) is a “state of the art privacy preserving tool”.
We are a high-tech software company, determined to transform how organizations handle sensitive data. We focus on uncompromised privacy and rapid deployment. We believe that robust privacy and business transformation go hand-in-hand.
The Virtual Data Lake is a distributed database. It helps teams to combine data silos and analyze them as one, without exposing the underlying data to each other.
Organizations can virtually combine data sets, run queries against this virtual database, with strong input privacy, and without the need to set up a new environment - avoiding technical and organizational hurdles.
Cranmera is a high performance decentralized data analytics engine. It is based on state of the art MPC technology. It is robust, scalable and allows for rapid application development and implementation.
Cranmera enables you to develop applications on top of performant MPC technology in weeks: Unlock the value of data sharing
without revealing your sensitive data.
Rosetta is a high-throughput parallel pseudonimisation solution, allowing different entities to use different pseudonyms for the same item. The separated items can be subjected to monitoring rules as if they are single entities.
The solution drastically reduces the risk of re-identification and data breaches.
To fight crime, information sharing between Government agencies and/or private sector organizations is crucial. Roseman Labs solutions enable this cooperation without compromising privacy or confidentiality. Our most recent use cases include sharing of sensitive cyber threat intel in a solution built for the Dutch National Cyber Security Center, and the exchange of sensitive information in human trafficking between law enforcement entities and an NGO.
With the help of smart meter data, grid operators can study the properties of their grid more effectively.
Together with Stedin and Technolution, we applied MPC technology to aggregate meter readings with strong privacy and security.
Roseman Labs built a solution that allows for combining anti-money laundering alerts across data silos. Our solution, Rosetta, achieves high-performance de-identification for multi-bank transaction monitoring that does not require HSMs or specialized hardware.
Analysis of patient data is key to advance our healthcare system. Today’s practices of pseudonymization are insecure, labor-intensive and insight destroying. With the Roseman Labs Virtual Data Lake, sources of different health providers can be joined virtually, without compromising privacy. Assess patient journeys, value and outcomes across providers in weeks instead of years.
State of the art cryptography ready to be used by data scientists
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